Triple
T15038742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hal Incandenza |
E378545
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Hal |
E60852
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Hal | Statement: [Hal Incandenza, nickname, Hal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hal Context triple: [Hal Incandenza, nickname, Hal]
-
A.
Hal
chosen
Hal is a masculine given name, commonly used as a diminutive form of Harold.
-
B.
Hel
Hel is the Norse goddess who rules over the underworld realm of the dead that bears her name.
-
C.
Hel
Hel is a town and popular seaside resort located at the tip of the Hel Peninsula on Poland’s Baltic coast.
-
D.
Han
Han is a common transliteration of the historical Central Asian title "Khan," often associated with rulers and nobility in various Turkic and Mongolic cultures.
-
E.
Han
Han refers to the majority ethnic group in China, historically associated with Chinese civilization, language, and culture.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded82cf3848190b0b2b6c9e65bc70b |
completed | April 15, 2026, 12:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9de1c3a48190a9b3959a95627175 |
completed | May 9, 2026, 2:37 a.m. |
Created at: April 10, 2026, 2:59 a.m.